Abstract
BackgroundGenomic sequence-based deduction of antibiotic minimum inhibitory concentration (MIC) has great potential to enhance the speed and sensitivity of antimicrobial susceptibility testing. We previously developed a penicillin-binding protein (PBP) typing system and two methods (Random Forest (RF) and Mode MIC (MM)) that accurately predicted β-lactam MICs for pneumococcal isolates carrying a characterized PBP sequence type (phenotypic β-lactam MICs known for at least one isolate of this PBP type). This study evaluates the prediction performance for previously uncharacterized (new) PBP types and the probability of encountering new PBP types, both of which impact the overall prediction accuracy.ResultsThe MM and RF methods were used to predict MICs of 4309 previously reported pneumococcal isolates in 2 datasets and the results were compared to the known broth microdilution MICs to 6 β-lactams. Based on a method that specifically evaluated predictions for new PBP types, the RF results were more accurate than MM results for new PBP types and showed percent essential agreement (MICs agree within ±1 dilution) >97%, percent category agreement (interpretive results agree) >93%, major discrepancy (sensitive isolate predicted as resistant) rate < 1.2%, and very major discrepancy (resistant isolate predicted as sensitive) rate < 1.4% for all 6 β-lactams. The identification of new PBP types over time was well approximated by a diminishingly increasing curve (Pearson’s r = 0.99) and minimally impacted overall MIC prediction performance.ConclusionsMIC prediction using the RF method could be an accurate alternative of phenotypic susceptibility testing even in the presence of previously uncharacterized PBP types.
Highlights
Genomic sequence-based deduction of antibiotic minimum inhibitory concentration (MIC) has great potential to enhance the speed and sensitivity of antimicrobial susceptibility testing
Given that vmj is a more serious discrepancy that should be minimized, the results indicated that β-lactam MIC predictions based on the Random Forest (RF) model were more suitable for a new penicillin-binding protein (PBP) type
Predictions of pneumococcal β-lactam MICs using the RF method could be an accurate alternative of phenotypic susceptibility testing even in the presence of previously uncharacterized PBP types
Summary
Genomic sequence-based deduction of antibiotic minimum inhibitory concentration (MIC) has great potential to enhance the speed and sensitivity of antimicrobial susceptibility testing. Sequence-based prediction of antimicrobial minimum inhibitory concentration (MIC) carries great potential to significantly improve clinical and public health microbiology [1, 2]. We previously have developed a “PBP typing” system based on sequence signatures in the TPD of the 3 PBPs to track and predict β-lactam minimum inhibitory concentration (MIC) in pneumococci [8,9,10]. The trained models accurately deduced β-lactam MICs of a test isolate if it carried a PBP type that was included in the training dataset (characterized PBP types) [8,9,10]. For a new PBP type that was not included (uncharacterized PBP types), the predicted MICs were generally less accurate, yet the performance has not been fully evaluated [9]
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